How to Obtain and Run Light and Efficient Deep Learning Networks

Speaker:        Prof. Yiran Chen
                Department of Electrical and Computer Engineering
                Duke University

Title:          "How to Obtain and Run Light and Efficient Deep
                 Learning Networks"

Date:           Friday, 5 July 2019

Time:           2:30pm - 4:00pm

Venue:          Room 6580 (via lift no. 27/28), HKUST

Abstract:

Fast growth of the computation cost associated with training and testing
of deep neural networks (DNNs) inspired various acceleration techniques.
Reducing topological complexity and simplifying data representation of
neural networks are two approaches that popularly adopted in deep learning
society: many connections in DNNs can be pruned and the precision of
synaptic weights can be reduced, respectively, incurring no or minimum
impact on inference accuracy. However, the practical impacts of hardware
design are often ignored in these algorithm-level techniques, such as the
increase of the random accesses to memory hierarchy and the constraints of
memory capacity. On the other side, the limited understanding about the
computational needs at algorithm level may lead to unrealistic assumptions
during the hardware designs. In this talk, we will discuss this mismatch
and show how we can solve it through an interactive design practice across
both software and hardware levels.


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Biography:

Yiran Chen received B.S and M.S. from Tsinghua University and Ph.D. from
Purdue University in 2005. After five years in industry, he joined
University of Pittsburgh in 2010 as Assistant Professor and then promoted
to Associate Professor with tenure in 2014, held Bicentennial Alumni
Faculty Fellow. He now is the Professor of the Department of Electrical
and Computer Engineering at Duke University and serving as the director of
NSF Industry--University Cooperative Research Center (IUCRC) for
Alternative Sustainable and Intelligent Computing (ASIC) and co-director
of Duke University Center for Computational Evolutionary Intelligence
(CEI), focusing on the research of new memory and storage systems, machine
learning and neuromorphic computing, and mobile computing systems. Prof.
Chen has published one book and more than 350 technical publications and
has been granted 94 US patents. He serves or served the associate editor
of several IEEE and ACM transactions/journals and served on the technical
and organization committees of more than 50 international conferences. He
received 6 best paper awards and 13 best paper nominations from
international conferences. He is the recipient of NSF CAREER award and ACM
SIGDA outstanding new faculty award. He is the Fellow of IEEE and
Distinguished Member of ACM, a distinguished lecturer of IEEE CEDA, and
the recipient of the Humboldt Research Fellowship for Experienced
Researchers.